In this study, from experiments and theoretical calculation, we reported that Ti 3 C 2 MXene can be applied as sensors for NH 3 detection at room temperature with high selectivity. Ti 3 C 2 MXene, a novel two-dimensional carbide, was prepared by etching off Al atoms from Ti 3 AlC 2 . The asprepared multilayer Ti 3 C 2 MXene powders were delaminated to a single layer by intercalation and ultrasonic dispersion. The colloidal suspension of single-layer Ti 3 C 2 -MXene was coated on the surface of ceramic tubes to construct sensors for gas detection. Thereafter, the sensors were used to detect various gases (CH 4 , H 2 S, H 2 O, NH 3 , NO, ethanol, methanol, and acetone) with a concentration of 500 ppm at room temperature. Ti 3 C 2 MXene-based sensors have high selectivity to NH 3 compared with other gases. The response to NH 3 was 6.13%, which was four times the second highest response (1.5% to ethanol gas). To understand the high selectivity, first-principles calculations were conducted to explore adsorption behaviors. From adsorption energy, adsorbed geometry, and charge transfer, it was confirmed that Ti 3 C 2 MXene theoretically has a high selectivity to NH 3 , compared with other gases in this experiment. Moreover, the response of the sensor to NH 3 increased almost linearly with NH 3 concentration from 10 to 700 ppm. The humidity tests and cycle tests of NH 3 showed that the Ti 3 C 2 MXenebased gas sensor has excellent performances for NH 3 detection at room temperature. KEYWORDS: two-dimensional materials, MXene, Ti 3 C 2 T x , room-temperature sensor, NH 3

Based on the radial modulation of electron-depleted shell layers in SnO 2 -ZnO core-shell nanofibers (CSNs), a novel approach is proposed for the detection of very low concentrations of reducing gases. In this work, SnO 2 -ZnO CSNs were synthesized by a two-step process: core SnO 2 nanofibers were first prepared by electrospinning, followed by the preparation of ZnO shell layers by atomic layer deposition. The radial modulation of electron depletion in the CSN shells was accomplished by controlling the shell thickness.The sensing capabilities of CSNs were investigated with respect to CO and NO 2 that represent typical reducing and oxidizing gases, respectively. In the case of CO at a critical shell thickness, the CSN-based sensors showed greatly improved sensing capabilities compared with those fabricated on the basis of either pure SnO 2 or pure ZnO nanofibers. In sharp contrast, CSN sensors revealed inferior sensing capabilities for NO 2 . The results can be explained by a model based on the radial modulation of the electron-depleted CSN shells. The model suggests that CSNs comprising dissimilar materials having different energy-band structures represent an effective sensing platform for the detection of low concentrations of reducing gases when the shell thickness is equivalent to the Debye length.

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Exploring reactions of methanol on TiO2 surfaces is of great importance in both C1 chemistry and photocatalysis. Reported herein is a combined experimental and theoretical calculation study of methanol adsorption and reaction on a mineral anatase TiO2(001)-(1×4) surface. The methanol-to-dimethyl ether (DME) reaction was unambiguously identified to occur by the dehydration coupling of methoxy species at the fourfold-coordinated Ti(4+) sites (Ti(4c)), and for the first time confirms the predicted higher reactivity of this facet compared to other reported TiO2 facets. Surface chemistry of methanol on the anatase TiO2(001)-(1×4) surface is seldom affected by co-chemisorbed water. These results not only greatly deepen the fundamental understanding of elementary surface reactions of methanol on TiO2 surfaces but also show that TiO2 with a high density of Ti(4c) sites is a potentially active and selective catalyst for the important methanol-to-DME reaction.

Sr2Cr3As2O2 is composed of alternating square-lattice CrO2 and Cr2As2 stacking layers, where CrO2 is isostructural to the CuO2 building-block of cuprate high-Tc superconductors and Cr2As2 to Fe2As2 of Fe-based superconductors. Current interest in this material is raised by theoretic prediction of possible superconductivity. In this neutron powder diffraction study, we discovered that magnetic moments of Cr(II) ions in the Cr2As2 sublattice develop a C-type antiferromagnetic structure below 590 K, and the moments of Cr(I) in the CrO2 sublattice form the La2CuO4-like antiferromagnetic order below 291 K. The staggered magnetic moment 2.19(4)µB /Cr(II) in the more itinerant Cr2As2 layer is smaller than 3.10(6)µB /Cr(I) in the more localized CrO2 layer. Different from previous expectation, a spin-flop transition of the Cr(II) magnetic order observed at 291 K indicates a strong coupling between the CrO2 and Cr2As2 magnetic subsystems.

Bi 2 O 3 -decorated In 2 O 3 nanorods were synthesized using a one-step process, and their structure, as well as the effects of decoration of In 2 O 3 nanorods with Bi 2 O 3 on the ethanol gassensing properties were examined. The multiple networked Bi 2 O 3decorated In 2 O 3 nanorod sensor showed responses of 171−1774% at ethanol concentrations of 10−200 ppm at 200 °C. The responses of the Bi 2 O 3 -decorated In 2 O 3 nanorod sensor were stronger than those of the pristine-In 2 O 3 nanorod sensors by 1.5− 4.9 times at the corresponding concentrations. The two sensors exhibited short response times and long recovery times. The optimal Bi concentration in the Bi 2 O 3 -decorated In 2 O 3 nanorod sensor and the optimal operation temperature of the sensor were 20% and 200 °C, respectively. The Bi 2 O 3 -decorated In 2 O 3 nanorod sensor showed selectivity for ethanol gas over other gases. The origin of the enhanced response, sensing speed, and selectivity for ethanol gas of the Bi 2 O 3 -decorated In 2 O 3 nanorod sensor to ethanol gas is discussed.

The strain-induced softening of thermoplastic polyurethane elastomers (TPUs), known as the Mullins effect, arises from their multi-phase structure. We used the combination of small- and wide- angle X-ray scattering (SAXS/WAXS) during in situ repeated tensile loading to elucidate the relationship between molecular architecture, nano-strain, and macro-scale mechanical properties. Insights obtained from our analysis highlight the importance of the ‘fuzzy interface’ between the hard and soft regions that governs the structure evolution at nanometre length scales and leads to macroscopic stiffness reduction. We propose a hierarchical Eshelby inclusion model of phase interaction mediated by the ‘fuzzy interface’ that accommodates the nano-strain gradient between hard and soft regions and undergoes tension-induced softening, causing the Mullins effect that becomes apparent in TPUs even at moderate tensile strains.

Abstract. We quantified the spatial distribution of roots of individual plants using detailed drawings from the literature of species of grasses, forbs, and shrubs in the Central Great Plains grasslands of North America. We scanned each two‐dimensional drawing electronically and used ARC/INFO, a Geographic Information System, to calculate root length (cm) and density (cm root length/cm soil) with depth in the soil profile. We then selected one of three mathematical models that best fit the data, and classified each species as either shallow‐, medium‐ or deep‐rooted. 66 root drawings from 55 species were evaluated. Most plants were shallow‐rooted with largest root densities occurring at depths < 20 cm; most maximum rooting depths were > 1m. Grasses had the shallowest maximum depth and shrubs the deepest. Deep‐rooted forbs had the smallest root densities by depth. Most plants had two sections to their distribution of root density: an initial increase from the soil surface followed by a decrease in density with increasing depth. Our results suggest that the abundance and importance of different species and growth forms in North American grasslands is related to similarities and differences in the spatial distributions of their root systems. Our approach provides quantitative information on root distributions to be used for comparisons among species, and in simulation modeling analyses that could not be done with conventional methods that are qualitative in nature.